


import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


A			:						Optional[Any]        =  'base_with_context'






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]	)	)
		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)
		for lyr_num, lyr in enumerate(model.encoders	):
				lowercase__	     =						weights[f'''layers_{lyr_num}''']
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						ly_weight["""attention"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)
		return model






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				str	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)

		for lyr_num, lyr in enumerate(model.encoders	):
				lowercase__	     =						weights[f'''layers_{lyr_num}''']
				lowercase__	     =						ly_weight["""attention"""]

				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)

		return model






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Tuple	)  ->    Union[str, Any]:
		"""simple docstring"""




		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T	)	)
		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)       ,       requires_grad=__magic_name__	)

		lowercase__	     =						nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T	)	)

		for lyr_num, lyr in enumerate(model.decoders	):
				lowercase__	     =						weights[f'''layers_{lyr_num}''']
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T	)	)

				lowercase__	     =						ly_weight["""self_attention"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

				lowercase__	     =						ly_weight["""MultiHeadDotProductAttention_0"""]
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]	)	)

				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)
				lowercase__	     =						nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
				lowercase__	     =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]	)	)

		lowercase__	     =						nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T	)	)

		return model






def 					UpperCamelCase   (      __magic_name__	:				int	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						checkpoints.load_tax_checkpoint(args.checkpoint_path	)
		lowercase__	     =						jnp.tree_util.tree_map(onp.array       ,       __magic_name__	)

		lowercase__	     =						[
		    """from __gin__ import dynamic_registration""",
		    """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
		    """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
		    """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
		]

		lowercase__	     =						os.path.join(args.checkpoint_path       ,       """.."""       ,       """config.gin"""	)
		lowercase__	     =						inference.parse_training_gin_file(__magic_name__       ,       __magic_name__	)
		lowercase__	     =						inference.InferenceModel(args.checkpoint_path       ,       __magic_name__	)

		lowercase__	     =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,       variance_type="""fixed_large"""	)

		lowercase__	     =						SpectrogramNotesEncoder(
		    max_length=synth_model.sequence_length["""inputs"""]       ,       vocab_size=synth_model.model.module.config.vocab_size       ,       d_model=synth_model.model.module.config.emb_dim       ,       dropout_rate=synth_model.model.module.config.dropout_rate       ,       num_layers=synth_model.model.module.config.num_encoder_layers       ,       num_heads=synth_model.model.module.config.num_heads       ,       d_kv=synth_model.model.module.config.head_dim       ,       d_ff=synth_model.model.module.config.mlp_dim       ,       feed_forward_proj="""gated-gelu"""       ,       )

		lowercase__	     =						SpectrogramContEncoder(
		    input_dims=synth_model.audio_codec.n_dims       ,       targets_context_length=synth_model.sequence_length["""targets_context"""]       ,       d_model=synth_model.model.module.config.emb_dim       ,       dropout_rate=synth_model.model.module.config.dropout_rate       ,       num_layers=synth_model.model.module.config.num_encoder_layers       ,       num_heads=synth_model.model.module.config.num_heads       ,       d_kv=synth_model.model.module.config.head_dim       ,       d_ff=synth_model.model.module.config.mlp_dim       ,       feed_forward_proj="""gated-gelu"""       ,       )

		lowercase__	     =						TaFilmDecoder(
		    input_dims=synth_model.audio_codec.n_dims       ,       targets_length=synth_model.sequence_length["""targets_context"""]       ,       max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time       ,       d_model=synth_model.model.module.config.emb_dim       ,       num_layers=synth_model.model.module.config.num_decoder_layers       ,       num_heads=synth_model.model.module.config.num_heads       ,       d_kv=synth_model.model.module.config.head_dim       ,       d_ff=synth_model.model.module.config.mlp_dim       ,       dropout_rate=synth_model.model.module.config.dropout_rate       ,       )

		lowercase__	     =						load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,       __magic_name__	)
		lowercase__	     =						load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,       __magic_name__	)
		lowercase__	     =						load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,       __magic_name__	)

		lowercase__	     =						OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""	)

		lowercase__	     =						SpectrogramDiffusionPipeline(
		    notes_encoder=__magic_name__       ,       continuous_encoder=__magic_name__       ,       decoder=__magic_name__       ,       scheduler=__magic_name__       ,       melgan=__magic_name__       ,       )
		if args.save:
				pipe.save_pretrained(args.output_path	)


if __name__ == "__main__":
	A			:						Tuple        =  argparse.ArgumentParser()

	parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
	parser.add_argument(
	    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default=F'{MODEL}/checkpoint_500000',
	    type=str,
	    required=False,
	    help='Path to the original jax model checkpoint.',
	)
	A			:						Optional[Any]        =  parser.parse_args()

	main(args)


_SCREAMING_SNAKE_CASE 		=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def 			lowercase(	)		->	None:







 '''simple docstring'''
 UpperCamelCase			  =      input("""Enter message: """       )
 UpperCamelCase			  =      input("""Enter key [alphanumeric]: """       )
 UpperCamelCase			  =      input("""Encrypt/Decrypt [e/d]: """       )

 if mode.lower().startswith("""e"""       ):
  UpperCamelCase			  =      """encrypt"""
  UpperCamelCase			  =      encrypt_message(UpperCamelCase_							,					UpperCamelCase_       )
 elif mode.lower().startswith("""d"""       ):
  UpperCamelCase			  =      """decrypt"""
  UpperCamelCase			  =      decrypt_message(UpperCamelCase_							,					UpperCamelCase_       )

 print(f"""\n{mode.title()}ed message:"""       )
 print(UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""encrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 return translate_message(UpperCamelCase_							,					UpperCamelCase_							,					"""decrypt"""       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      []
 UpperCamelCase			  =      0
 UpperCamelCase			  =      key.upper()

 for symbol in message:
  UpperCamelCase			  =      LETTERS.find(symbol.upper()       )
  if num != -1:
   if mode == "encrypt":
    num += LETTERS.find(key[key_index]       )
   elif mode == "decrypt":
    num -= LETTERS.find(key[key_index]       )

   num %= len(UpperCamelCase_       )

   if symbol.isupper():
    translated.append(LETTERS[num]       )
   elif symbol.islower():
    translated.append(LETTERS[num].lower()       )

   key_index += 1
   if key_index == len(UpperCamelCase_       ):
    UpperCamelCase			  =      0
  else:
   translated.append(UpperCamelCase_       )
 return "".join(UpperCamelCase_       )


if __name__ == "__main__":
     main()
